Determining the 95% limit of detection for waterborne pathogen analyses from primary concentration to qPCR

• We present an approach to establish the limit of detection for qPCR-based analyses.
• The approach is empirical and represents all processing steps included in analysis.
• Focusing on concentrations near the limit of detection minimizes analyses needed.
• An equation calculates technical replicates needed for a desired limit of detection.
• For illustration, we determine the limits of detection for 3 pathogens in tap water.

The limit of detection (LOD) for qPCR-based analyses is not consistently defined or determined in studies on waterborne pathogens. Moreover, the LODs reported often reflect the qPCR assay alone rather than the entire sample process. Our objective was to develop an approach to determine the 95% LOD (lowest concentration at which 95% of positive samples are detected) for the entire process of waterborne pathogen detection. We began by spiking the lowest concentration that was consistently positive at the qPCR step (based on its standard curve) into each procedural step working backwards (i.e., extraction, secondary concentration, primary concentration), which established a concentration that was detectable following losses of the pathogen from processing. Using the fraction of positive replicates (n = 10) at this concentration, we selected and analyzed a second, and then third, concentration. If the fraction of positive replicates equaled 1 or 0 for two concentrations, we selected another. We calculated the LOD using probit analysis. To demonstrate our approach we determined the 95% LOD for Salmonella enterica serovar Typhimurium, adenovirus 41, and vaccine-derived poliovirus Sabin 3, which were 11, 12, and 6 genomic copies (gc) per reaction (rxn), respectively (equivalent to 1.3, 1.5, and 4.0 gc L−1 assuming the 1500 L tap-water sample volume prescribed in EPA Method 1615). This approach limited the number of analyses required and was amenable to testing multiple genetic targets simultaneously (i.e., spiking a single sample with multiple microorganisms). An LOD determined this way can facilitate study design, guide the number of required technical replicates, aid method evaluation, and inform data interpretation.

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